| // Copyright (C) 2016 Davis E. King ([email protected]) | |
| // License: Boost Software License See LICENSE.txt for the full license. | |
| namespace dlib | |
| { | |
| // ---------------------------------------------------------------------------------------- | |
| double log1pexp( | |
| double x | |
| ); | |
| /*! | |
| ensures | |
| - returns log(1+exp(x)) | |
| (except computes it using a numerically accurate method) | |
| NOTE: For technical reasons, it is defined in misc.h. | |
| !*/ | |
| // ---------------------------------------------------------------------------------------- | |
| void randomize_parameters ( | |
| tensor& params, | |
| unsigned long num_inputs_and_outputs, | |
| dlib::rand& rnd | |
| ); | |
| /*! | |
| ensures | |
| - This function assigns random values into params based on the given random | |
| number generator. In particular, it uses the parameter initialization method | |
| of formula 16 from the paper "Understanding the difficulty of training deep | |
| feedforward neural networks" by Xavier Glorot and Yoshua Bengio. | |
| - It is assumed that the total number of inputs and outputs from the layer is | |
| num_inputs_and_outputs. That is, you should set num_inputs_and_outputs to | |
| the sum of the dimensionalities of the vectors going into and out of the | |
| layer that uses params as its parameters. | |
| !*/ | |
| // ---------------------------------------------------------------------------------------- | |
| template <typename net_type> | |
| void net_to_xml ( | |
| const net_type& net, | |
| std::ostream& out | |
| ); | |
| /*! | |
| requires | |
| - net_type is an object of type add_layer, add_loss_layer, add_skip_layer, or | |
| add_tag_layer. | |
| - All layers in the net must provide to_xml() functions. | |
| ensures | |
| - Prints the given neural network object as an XML document to the given output | |
| stream. | |
| !*/ | |
| template <typename net_type> | |
| void net_to_xml ( | |
| const net_type& net, | |
| const std::string& filename | |
| ); | |
| /*! | |
| requires | |
| - net_type is an object of type add_layer, add_loss_layer, add_skip_layer, or | |
| add_tag_layer. | |
| - All layers in the net must provide to_xml() functions. | |
| ensures | |
| - This function is just like the above net_to_xml(), except it writes to a file | |
| rather than an ostream. | |
| !*/ | |
| // ---------------------------------------------------------------------------------------- | |
| template <typename net_type> | |
| dpoint input_tensor_to_output_tensor( | |
| const net_type& net, | |
| dpoint p | |
| ); | |
| /*! | |
| requires | |
| - net_type is an object of type add_layer, add_skip_layer, or add_tag_layer. | |
| - All layers in the net must provide map_input_to_output() functions. | |
| ensures | |
| - Given a dpoint (i.e. a row,column coordinate) in the input tensor given to | |
| net, this function returns the corresponding dpoint in the output tensor | |
| net.get_output(). This kind of mapping is useful when working with fully | |
| convolutional networks as you will often want to know what parts of the | |
| output feature maps correspond to what parts of the input. | |
| - If the network contains skip layers then any layers skipped over by the skip | |
| layer are ignored for the purpose of computing this coordinate mapping. That | |
| is, if you walk the network from the output layer to the input layer, where | |
| each time you encounter a skip layer you jump to the layer indicated by the | |
| skip layer, you will visit exactly the layers in the network involved in the | |
| input_tensor_to_output_tensor() calculation. This behavior is useful since it | |
| allows you to compute some auxiliary DNN as a separate branch of computation | |
| that is separate from the main network's job of running some kind of fully | |
| convolutional network over an image. For instance, you might want to have a | |
| branch in your network that computes some global image level | |
| summarization/feature. | |
| !*/ | |
| // ---------------------------------------------------------------------------------------- | |
| template <typename net_type> | |
| dpoint output_tensor_to_input_tensor( | |
| const net_type& net, | |
| dpoint p | |
| ); | |
| /*! | |
| requires | |
| - net_type is an object of type add_layer, add_skip_layer, or add_tag_layer. | |
| - All layers in the net must provide map_output_to_input() functions. | |
| ensures | |
| - This function provides the reverse mapping of input_tensor_to_output_tensor(). | |
| That is, given a dpoint in net.get_output(), what is the corresponding dpoint | |
| in the input tensor? | |
| !*/ | |
| // ---------------------------------------------------------------------------------------- | |
| template <typename net_type> | |
| inline size_t count_parameters( | |
| const net_type& net | |
| ); | |
| /*! | |
| requires | |
| - net_type is an object of type add_layer, add_loss_layer, add_skip_layer, or | |
| add_tag_layer. | |
| ensures | |
| - Returns the number of allocated parameters in the network. E.g. if the network has not | |
| been trained then, since nothing has been allocated yet, it will return 0. | |
| !*/ | |
| // ---------------------------------------------------------------------------------------- | |
| template<typename net_type> | |
| void set_all_learning_rate_multipliers( | |
| net_type& net, | |
| double learning_rate_multiplier | |
| ); | |
| /*! | |
| requires | |
| - net_type is an object of type add_layer, add_loss_layer, add_skip_layer, or | |
| add_tag_layer. | |
| - learning_rate_multiplier >= 0 | |
| ensures | |
| - Sets all learning_rate_multipliers and bias_learning_rate_multipliers in net | |
| to learning_rate_multiplier. | |
| !*/ | |
| // ---------------------------------------------------------------------------------------- | |
| template <size_t begin, size_t end, typename net_type> | |
| void set_learning_rate_multipliers_range( | |
| net_type& net, | |
| double learning_rate_multiplier | |
| ); | |
| /*! | |
| requires | |
| - net_type is an object of type add_layer, add_loss_layer, add_skip_layer, or | |
| add_tag_layer. | |
| - learning_rate_multiplier >= 0 | |
| - begin <= end <= net_type::num_layers | |
| ensures | |
| - Loops over the layers in the range [begin,end) in net and calls | |
| set_learning_rate_multiplier on them with the value of | |
| learning_rate_multiplier. | |
| !*/ | |
| // ---------------------------------------------------------------------------------------- | |
| } | |